Reddit Pain Finder - Find Real User Problems, Not Noise

Reddit Pain Finder is a Reddit scraper for finding real Reddit pain points, user complaints, product frustrations, and market research signals. It filters out promo noise, classifies pain types with deterministic rules, and ranks the results so you can focus on the best opportunities first.

You can run it with zero setup in public mode using Reddit RSS plus public JSON, or switch to the official Reddit OAuth API for better reliability, body text, and comments at scale.

Why most Reddit scrapers fail

Most Reddit scraping tools still return irrelevant results because:

• Reddit search is relevance-based, not strict keyword-based
• results often include promo posts, launch posts, and generic chatter
• matching is weak, so posts do not actually contain the keyword you care about
• output is not ranked for urgency or actionability

That leads to inconsistent datasets and a lot of manual cleanup.

Why Reddit Pain Finder is different

• Filters out promo and low-value noise automatically
• Classifies posts into pain_report, question, promo, case_study, or other
• Supports strict keyword filtering, including title-only mode
• Scores results by pain strength, engagement, and recency
• Works in public mode first, with optional Reddit API mode for scale
• Produces automation-friendly output for datasets, webhooks, and workflows

What you get in one run

• "I'm tired of switching between tools just to send emails" -> painType: workflow_friction, priorityScore: 32
• "Best email marketing tool for startups?" -> painType: question, priorityScore: 24
• "I built an email SaaS - beta testers wanted" -> filtered out as promo

🔎 Fix Reddit search noise

If you are tired of irrelevant Reddit matches, enable strict filtering:

{
  "subreddits": ["SaaS", "startups"],
  "keywords": ["email"],
  "matchMode": "title",
  "matchType": "contains",
  "titleMustContainKeyword": true,
  "minMatchCount": 1,
  "outputMode": "strict"
}

Only posts with email in the title will be returned.

🎯 Who is this for?

• SaaS founders validating ideas from real user complaints
• Indie hackers looking for recurring pain points
• Product teams prioritizing features and fixes
• Growth and research teams monitoring real user problems
• Competitive intelligence workflows tracking switching behavior and unmet needs

⚖️ Compared to typical Reddit scrapers

Feature	Typical scraper	Reddit Pain Finder
Exact keyword control	No	Yes
Title-only strict filtering	No	Yes
Pain vs promo filtering	No	Yes
Deterministic pain classification	Rarely	Yes
Priority scoring	Rarely	Yes
Dataset and webhook automation	Sometimes	Yes

🚀 Why use Reddit Pain Finder?

• Product and market research: find complaints about missing features, bugs, pricing, and workflow friction
• Competitive intelligence: see which pain types dominate specific subreddits
• Prioritization: every item includes confidence and priorityScore
• Low friction: no API key required to start
• Actionable output: sort by priorityScore >= 20 to surface the strongest discussions quickly

Together with the Apify platform, you also get scheduling, monitoring, API access, webhooks, and integrations with tools like n8n, Make, and Zapier.

🧠 What the Actor does

Dual-mode Reddit data source

Mode	Data source
Public (default)	Reddit RSS feeds plus public thread .json
API	Official Reddit OAuth API when credentials are provided

Automatic mode selection

• Chooses public or API mode at startup based on input
• Lets you force a mode with forceMode
• If you force public mode but also provide Reddit API credentials, repeated public 403 responses can trigger an automatic retry in API mode

Post type filtering and noise reduction

Each post is classified as one of:

• pain_report
• question
• promo
• case_study
• other

By default, only pain_report and question are included, while promo posts like "I built", "beta testers wanted", or "roast my SaaS" are excluded.

Pain classification for product research

• Labels each post with a pain type such as missing_feature, pricing_cost, workflow_friction, integration_need, or switching_alternative
• Uses weighted phrase and pattern rules, not an LLM
• Calculates confidence from evidence strength, field location, and separation from competing candidates

Priority scoring and deduplication

priorityScore combines:

• confidence
• engagement
• recency

It still works for fresh posts with zero engagement, downranks promo and case-study content, and can deduplicate within a run and across recent runs.

Optional comments analysis

• Fetch and analyze comments
• Traverses nested Reddit reply trees, not just top-level comments
• Return raw comments when enabled
• Include up to five comment samples
• Count how many comments contain pain signals

Public body enrichment and run summaries

• fetchPostBodiesInPublicMode can enrich bodyText in public mode even when includeComments is off
• publicThreadSuccessRateWarningThreshold warns when Reddit blocking degrades public thread coverage too far
• failOnLowPublicThreadSuccessRate can stop the run instead of returning low-quality title-only public results
• Stores the latest run summary in key-value store record RUN_SUMMARY
• Summary includes top pain types, top subreddits, inclusion counts, keyword-match stats, and public-fetch quality warnings for downstream automation

Optional webhook delivery

• Push result batches to your own endpoint
• Configure retries and timeouts
• Add authentication headers or HMAC signing
• Optionally store failed webhook batches in key-value storage

Consistent output schema

• Same output shape in public and API modes
• Every item includes sourceMode so downstream systems know how it was fetched
• When Apify Store per-result pricing is active, the default dataset contains billable included results only

Store pricing behavior

• Under the synthetic apify-default-dataset-item pricing event, the Actor writes only included results to the default dataset
• Filtered-out rows are still counted in RUN_SUMMARY, but they are omitted from the default dataset so users are not billed for non-results
• The Actor also respects the run's paid result limit and stops pushing additional billable rows when that limit is reached
• Legacy pay-per-result runs using ACTOR_MAX_PAID_DATASET_ITEMS are also respected

🧩 Strict keyword filtering and relevance

This layer runs after scraping, so the Actor does not rely on Reddit search accuracy.

• keywords: the internal terms to match
• matchMode: check title, body, or title_or_body
• matchType: use contains, exact, or regex
• caseSensitive: control normalization
• minMatchCount: require a minimum number of matches
• titleMustContainKeyword: enforce the title-only gate
• outputMode: choose strict, all, or scored
• debug: log rejections and include rejectionReason

Each output item can include:

• match.matched
• match.matchedKeywords
• match.matchCount
• match.matchedFields
• match.strictPassed
• score
• rejectionReason when debug mode is enabled

📦 What data can Reddit Pain Finder extract?

Field	Description
subreddit	Subreddit name
postId	Reddit post ID
postUrl	URL to the post
title	Post title
bodyText	Post body text
author	Post author
createdUtc	Post creation time in ISO format
scoreUpvotes	Upvote count
numComments	Comment count
painType	Classified pain category
painSummary	Short summary from strongest signals
painSignals	Matched pain phrases
matchedKeywords	Compatibility keyword field
match	Strict keyword match metadata
score	Keyword relevance score
confidence	Classification confidence from 0 to 1
priorityScore	Priority score from 0 to 100
postType	Post intent label
postTypeSignals	Signals that determined post type
commentSamples	Up to five comment samples
commentSignals	Pain phrases found in comments
commentPainMentions	Number of comments with pain signals
comments	Raw comment objects when comments are enabled
includedByFilter	Whether the post passed post-type filters
filterReason	Why the post was filtered out
sourceMode	public or api
rejectionReason	Why strict keyword filtering rejected the post when debug: true

When debug: true, items can also include painScores, topCandidates, evidenceSummary, and priorityDebug.

⚡ How to use Reddit Pain Finder

1. Open the Actor in Apify.
2. Enter one or more subreddits such as SaaS or startups.
3. Choose sort, time filter, and limits.
4. Optionally enable comments with includeComments: true.
5. Optionally add Reddit API credentials for scale and reliability.
6. Run the Actor and review the dataset output.

Public mode vs Reddit API mode

Mode	When to use	Data source
Public (default)	Quick runs, no credentials	RSS plus public thread JSON
API	Reliable body text and comments, higher limits	Official Reddit OAuth API

Public mode is ideal for quick validation. API mode is better for repeated runs, richer post bodies, and reliable comments.

Reliability notes

• Reddit often returns 403 for public thread JSON from cloud or datacenter IPs
• In public mode, the Actor uses got-scraping
• You can improve public-mode reliability with Apify Proxy
• If public mode starts returning repeated 403s and API credentials are configured, adaptive fallback can retry via API automatically
• If public thread success drops below publicThreadSuccessRateWarningThreshold, the Actor logs a strong quality warning and records it in RUN_SUMMARY
• If failOnLowPublicThreadSuccessRate is true, the run fails instead of emitting degraded public-only results
• For the strongest reliability, use API mode

Post type and quality controls

• includePostTypes defaults to ["pain_report","question"]
• excludePostTypes defaults to ["promo"]
• promoDownrankFactor defaults to 0.25
• minAgeMinutes defaults to 0
• debug defaults to false
• enablePriorityFloor defaults to true

These controls let you tune noise, freshness, and ranking without changing the core logic.

Advanced performance controls

• publicSubredditConcurrency and publicThreadConcurrency override public-mode auto tuning
• apiSubredditConcurrency and apiCommentConcurrency override API-mode auto tuning
• Leave them empty unless you need to tune throughput for a specific workload

Recommended first run

{
  "subreddits": ["SaaS", "startups"],
  "sort": "new",
  "limitPerSubreddit": 25,
  "includeComments": false
}

Then sort the output by priorityScore and inspect the top five to ten items first.

🛠️ Input examples

Minimal public run

{
  "subreddits": ["SaaS", "startups"],
  "sort": "new",
  "limitPerSubreddit": 25
}

API mode

{
  "forceMode": "api",
  "redditClientId": "YOUR_CLIENT_ID",
  "redditClientSecret": "YOUR_CLIENT_SECRET",
  "redditRefreshToken": "YOUR_REFRESH_TOKEN",
  "subreddits": ["SaaS", "startups"],
  "sort": "top",
  "timeFilter": "week"
}

Public mode with Apify Proxy

{
  "subreddits": ["SaaS", "startups"],
  "includeComments": true,
  "useApifyProxy": true
}

Public mode with body enrichment and API fallback

{
  "forceMode": "public",
  "redditClientId": "YOUR_CLIENT_ID",
  "redditClientSecret": "YOUR_CLIENT_SECRET",
  "redditRefreshToken": "YOUR_REFRESH_TOKEN",
  "subreddits": ["SaaS", "startups"],
  "includeComments": false,
  "fetchPostBodiesInPublicMode": true,
  "autoFallbackToApiOnPublic403": true,
  "public403FallbackThreshold": 3,
  "publicThreadSuccessRateWarningThreshold": 0.6
}

Public mode that fails on low coverage

{
  "forceMode": "public",
  "subreddits": ["SaaS"],
  "includeComments": false,
  "fetchPostBodiesInPublicMode": true,
  "useApifyProxy": true,
  "publicThreadSuccessRateWarningThreshold": 0.75,
  "failOnLowPublicThreadSuccessRate": true
}

Strict title filtering

{
  "subreddits": ["SaaS", "startups"],
  "keywords": ["email", "newsletter", "email marketing"],
  "matchMode": "title",
  "matchType": "contains",
  "titleMustContainKeyword": true,
  "minMatchCount": 1,
  "outputMode": "strict"
}

Store credentials in Apify secrets. Apify Proxy requires an Apify Proxy subscription.

✅ Output example

Included item

{
  "subreddit": "SaaS",
  "postId": "abc123",
  "title": "Best email marketing tool for startups?",
  "createdUtc": "2025-02-14T12:00:00.000Z",
  "painType": "question",
  "confidence": 0.72,
  "priorityScore": 35,
  "score": 5,
  "match": {
    "matched": true,
    "matchedKeywords": ["email"],
    "matchCount": 1,
    "matchedFields": ["title"],
    "strictPassed": true
  },
  "postType": "question",
  "includedByFilter": true,
  "sourceMode": "public"
}

Filtered-out item

{
  "subreddit": "SaaS",
  "postId": "xyz789",
  "title": "I built an email SaaS - beta testers wanted",
  "painType": "unknown",
  "postType": "promo",
  "score": 0,
  "match": {
    "matched": true,
    "matchedKeywords": ["email"],
    "matchCount": 1,
    "matchedFields": ["title"],
    "strictPassed": true
  },
  "includedByFilter": false,
  "filterReason": "postType excluded: promo",
  "sourceMode": "public"
}

You can download the full dataset as JSON, CSV, Excel, or HTML from the Actor run dataset in Apify Console.

💡 Example usage scenarios

Find the strongest pain reports

Sort by priorityScore descending and focus on:

• priorityScore >= 25
• postType = "pain_report"

This is a strong first pass for product discovery and feature prioritization.

Discover pricing pain

Filter for:

• painType = "pricing_cost"
• priorityScore >= 20

This is useful for pricing strategy, finance tools, and cost-control workflows.

Detect switching behavior

Filter for:

• painType = "switching_alternative"
• confidence >= 0.6

Then inspect painSummary and matchedKeywords to understand dissatisfaction and buying intent.

Ignore low-signal threads

Ignore items where:

• painType = "unknown"
• priorityScore < 10

This removes general discussion, weak polls, and low-signal chatter.

Use questions as early indicators

Look for:

• postType = "question"
• priorityScore between 15 and 25
• painType != "unknown"

These often appear before switching, churn, or feature-demand events become explicit.

Automation-ready workflow

• Schedule the Actor daily or weekly
• Send batches to a webhook or automation tool
• Trigger downstream actions when priorityScore >= 25

Common downstream destinations include Slack, Airtable, Notion, internal dashboards, and enrichment pipelines.

The RUN_SUMMARY key-value store record is useful when you want a compact machine-readable health and results summary without scanning the full dataset.

When public enrichment is attempted, RUN_SUMMARY can also include:

• publicFetchQuality.attemptedThreadFetches
• publicFetchQuality.successfulThreadFetches
• publicFetchQuality.threadFetchSuccessRate
• publicFetchQuality.bodyTextCoverageRate
• warnings

When Store pricing limits are reached, RUN_SUMMARY.counts.skippedByBudget records how many additional billable rows were withheld from the default dataset.

📈 How to interpret priorityScore

Priority score	Meaning
35+	Strong, explicit pain; investigate immediately
25-34	Clear pain or high-value question
15-24	Early signal worth monitoring
<10	Noise or weak signal

🔗 Integrations

You can use Reddit Pain Finder with n8n, Make, Zapier, OpenClaw, or any system that can call HTTP APIs or receive webhooks.

• n8n: use the Apify node or the .actor/integrations/n8n-reddit-pain-finder-example.json
• Make: use the Apify module
• Zapier: use the Apify app
• Webhook: set webhookUrl in Actor input to receive POST batches of results

For webhook payload details and setup guidance, see INTEGRATIONS.md.

🛡️ Legal and limitations

• Public content only; no private or non-public data
• No LLM; classification is rule-based and deterministic
• Rate limits still apply
• Reddit behavior and access rules can change over time

Always ensure your usage complies with Reddit's User Agreement, Reddit Data API Terms, and applicable laws such as GDPR.

💬 Support and feedback

• Use the Issues tab on the Actor page for bugs or feature requests
• Feedback is especially welcome for new pain patterns, subreddit packs, and filtering ideas

⭐ Rate this Actor

If Reddit Pain Finder saves you time or helps you find better pain signals, please rate the Actor in Apify Console and leave a short review. Ratings help other users quickly see that the Actor is reliable for real-world Reddit research workflows.

Final note

All classification and scoring is deterministic, explainable, and stable.

Reddit Pain Finder is built to surface signal, not hype.